{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing Plot Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\n",
    "We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\n",
    "\n",
    "The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XAGXK+BKjlw4etBb6o4/aarOsfPHJRRfZk+3I+hfbtvkdUdQxsceigwet7ql9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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 1000)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, np.sin(x), '-b', label='Sine')\n",
    "ax.plot(x, np.cos(x), '--r', label='Cosine')\n",
    "ax.axis('equal')\n",
    "leg = ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there are many ways we might want to customize such a legend.\n",
    "For example, we can specify the location and turn off the frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fsuDHHwOPPBL1zdp164CrrrKld1essKUXyEFlylh5SYsWQMOGNpuuEGFij0TF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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(loc='upper left', frameon=False)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the ``ncol`` command to specify the number of columns in the legend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UinD2sf8DwLQwno+IiIJQ9HQHiMgMAGef+CMACmCgqk7JPWYggCxVTcnvXMnJ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\niBzDxE5E5Jj/B+vIEdfZxz8dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(frameon=False, loc='lower center', ncol=2)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EKmZOmQKcf77bEXmIqucGKL7+GrjrLlt8e8stnO4YfdassXluDzxgb9dRThV4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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on available legend options, see the ``plt.legend`` docstring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing Elements for the Legend\n",
    "\n",
    "As we have already seen, the legend includes all labeled elements by default.\n",
    "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\n",
    "The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\n",
    "Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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\ncePGTS3ErfzduHHjphby/7xsYKsXV7EBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d779518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\n",
    "lines = plt.plot(x, y)\n",
    "\n",
    "# lines is a list of plt.Line2D instances\n",
    "plt.legend(lines[:2], ['first', 'second']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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\ncePGTS3ErfzduHHjphby/7xsYKsXV7EBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11025d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y[:, 0], label='first')\n",
    "plt.plot(x, y[:, 1], label='second')\n",
    "plt.plot(x, y[:, 2:])\n",
    "plt.legend(framealpha=1, frameon=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that by default, the legend ignores all elements without a ``label`` attribute set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legend for Size of Points\n",
    "\n",
    "Sometimes the legend defaults are not sufficient for the given visualization.\n",
    "For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n",
    "Here is an example where we'll use the size of points to indicate populations of California cities.\n",
    "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+EGzevJm0tDTWrVvH448/PuD9dYVCQtpGIoZJp4d4m3zs/fAIlnHxWC1m6isb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d74cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cities = pd.read_csv('data/california_cities.csv')\n",
    "\n",
    "# Extract the data we're interested in\n",
    "lat, lon = cities['latd'], cities['longd']\n",
    "population, area = cities['population_total'], cities['area_total_km2']\n",
    "\n",
    "# Scatter the points, using size and color but no label\n",
    "plt.scatter(lon, lat, label=None,\n",
    "            c=np.log10(population), cmap='viridis',\n",
    "            s=area, linewidth=0, alpha=0.5)\n",
    "plt.axis(aspect='equal')\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar(label='log$_{10}$(population)')\n",
    "plt.clim(3, 7)\n",
    "\n",
    "# Here we create a legend:\n",
    "# we'll plot empty lists with the desired size and label\n",
    "for area in [100, 300, 500]:\n",
    "    plt.scatter([], [], c='k', alpha=0.3, s=area,\n",
    "                label=str(area) + ' km$^2$')\n",
    "plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\n",
    "\n",
    "plt.title('California Cities: Area and Population');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\n",
    "In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\n",
    "Notice too that the legend only lists plot elements that have a label specified.\n",
    "\n",
    "By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\n",
    "This strategy can be useful for creating more sophisticated visualizations.\n",
    "\n",
    "Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\n",
    "For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Legends\n",
    "\n",
    "Sometimes when designing a plot you'd like to add multiple legends to the same axes.\n",
    "Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\n",
    "If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\n",
    "We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvEG3b98u9s0wGfz9/WnKlCl0/PhxsrGxKbZt0RkyJo/TarX06aefUocOHYpF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\n2BljzMpwYmeMMSvz/8NbTOcDCpp6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f1e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "lines = []\n",
    "styles = ['-', '--', '-.', ':']\n",
    "x = np.linspace(0, 10, 1000)\n",
    "\n",
    "for i in range(4):\n",
    "    lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n",
    "                     styles[i], color='black')\n",
    "ax.axis('equal')\n",
    "\n",
    "# specify the lines and labels of the first legend\n",
    "ax.legend(lines[:2], ['line A', 'line B'],\n",
    "          loc='upper right', frameon=False)\n",
    "\n",
    "# Create the second legend and add the artist manually.\n",
    "from matplotlib.legend import Legend\n",
    "leg = Legend(ax, lines[2:], ['line C', 'line D'],\n",
    "             loc='lower right', frameon=False)\n",
    "ax.add_artist(leg);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a peek into the low-level artist objects that comprise any Matplotlib plot.\n",
    "If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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